The classified side-match vector quantizer, CSMVQ, has already been presented for low-bit-rate image encoding. It exploits a block classifier to decide which class the input vector belongs to using the variances of the upper and left codewords. However, this block classifier doesn't take the variance of the current input vector itself into account. This letter presents a new CSMVQ in which a two-level block classifier is used to classify input vectors and two different master codebooks are used for generating the state codebook according to the variance of the input vector. Experimental results prove the effectiveness of the proposed CSMVQ.

Self embedding watermarking is a technique used for tamper detection, localization and recovery. This letter proposes a novel self embedding scheme, in which the halftone version of the host image is exploited as a watermark, instead of a JPEG-compressed version used in most existing methods. Our scheme employs a pixel-wise permuted and embedded mechanism and thus overcomes some common drawbacks of the previous methods. Experimental results demonstrate our technique is effective and practical.

Small-world is a common property existing in many real-life social, technological and biological networks. Small-world networks distinguish themselves from others by their high clustering coefficient and short average path length. In the past dozen years, many probabilistic small-world networks and some deterministic small-world networks have been proposed utilizing various mechanisms. In this Letter, we propose a new deterministic small-world network model by first constructing a binary-tree structure and then adding links between each pair of brother nodes and links between each grandfather node and its four grandson nodes. Furthermore, we give the analytic solutions to several topological characteristics, which shows that the proposed model is a small-world network.

Until today, digital image watermarking has not been large-scale used in the industry. The first reason is that the watermarking efficiency is low and the real-time performance cannot be satisfied. The second reason is that the watermarking scheme cannot cope with various attacks. To solve above problems, this paper presents a multi-domain based digital image watermarking scheme, where a fast DFT (Discrete Fourier Transform) based watermarking method is proposed for synchronization correction and an IWT-DCT (Integer Wavelet Transform-Discrete Cosine Transform) based watermarking method is proposed for information embedding. The proposed scheme has high efficiency during embedding and extraction. Compared with five existing schemes, the robustness of our scheme is very strong and our scheme can cope with many common attacks and compound attacks, and thus can be used in wide application scenarios.

This paper presents a novel digital image watermarking algorithm based on the labeled bisecting clustering technique. Each cluster is labeled either '0' or '1' based on the labeling key. Each input image block is then assigned to the nearest codeword or cluster centre whose label is equal to the watermark bit. The watermark extraction can be performed blindly. The proposed method is robust to JPEG compression and some spatial-domain processing operations. Simulation results demonstrate the effectiveness of the proposed algorithm.

This Letter proposes a new kind of features for color image retrieval based on Distance-weighted Boundary Predictive Vector Quantization (DWBPVQ) Index Histograms. For each color image in the database, 6 histograms (2 for each color component) are calculated from the six corresponding DWBPVQ index sequences. The retrieval simulation results show that, compared with the traditional Spatial-domain Color-Histogram-based (SCH) features and the DCTVQ index histogram-based (DCTVQIH) features, the proposed DWBPVQIH features can greatly improve the recall and precision performance.

This Letter proposes a autoencoder model supervised by semantic similarity for zero-shot learning. With the help of semantic similarity vectors of seen and unseen classes and the classification branch, our experimental results on two datasets are 7.3% and 4% better than the state-of-the-art on conventional zero-shot learning in terms of the averaged top-1 accuracy.

This Letter focuses on deep learning-based monkeys' head swing counting problem. Nowadays, there are very few papers on monkey detection, and even fewer papers on monkeys' head swing counting. This research tries to fill in the gap and try to calculate the head swing frequency of monkeys through deep learning, where we further extend the traditional target detection algorithm. After analyzing object detection results, we localize the monkey's actions over a period. This Letter analyzes the task of counting monkeys' head swings, and proposes the standard that accurately describes a monkey's head swing. Under the guidance of this standard, the monkeys' head swing counting accuracy in 50 test videos reaches 94.23%.

This paper presents an image watermarking method for two purposes: to notify the copyright owner with a visible watermark, and to protect the copyright with an invisible watermark. These two watermarks are embedded in different blocks with different methods. Simulation results show that the visible watermark is hard to remove and the invisible watermark is robust.

Reversible data hiding has been a hot research topic since both the host media and hidden data can be recovered without distortion. In the past several years, more and more attention has been paid to reversible data hiding schemes for images in compressed formats such as JPEG, JPEG2000, Vector Quantization (VQ) and Block Truncation Coding (BTC). Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have no ability to reduce the bit rate but may reduce the image quality. This paper presents a novel reversible data hiding scheme for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, the original BTC technique is performed on the original image to obtain the BTC-compressed data which can be represented by a high mean table, a low mean table and a bitplane sequence. Then, the proposed reversible data hiding scheme is performed on both the high mean table and low mean table. Our hiding scheme is a lossless joint hiding and compression method based on 22 blocks in mean tables, thus it can not only hide data in mean tables but also reduce the bit rate. Experiments show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity and efficiency.

In this Letter, a new face recognition approach based on curvelets and local ternary patterns (LTP) is proposed. First, we observe that the curvelet transform is a new anisotropic multi-resolution transform and can efficiently represent edge discontinuities in face images, and that the LTP operator is one of the best texture descriptors in terms of characterizing face image details. This motivated us to decompose the image using the curvelet transform, and extract the features in different frequency bands. As revealed by curvelet transform properties, the highest frequency band information represents the noisy information, so we directly drop it from feature selection. The lowest frequency band mainly contains coarse image information, and thus we deal with it more precisely to extract features as the face's details using LTP. The remaining frequency bands mainly represent edge information, and we normalize them for achieving explicit structure information. Then, all the extracted features are put together as the elementary feature set. With these features, we can reduce the features' dimension using PCA, and then use the sparse sensing technique for face recognition. Experiments on the Yale database, the extended Yale B database, and the CMU PIE database show the effectiveness of the proposed methods.

Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.

In this Letter, we propose a novel method of low-level global motion feature description based on Vector Quantization (VQ) index histograms of motion feature vectors (MFVVQIH) for the purpose of video shot retrieval. The contribution lies in three aspects: first, we use VQ to eliminate singular points in the motion feature vector space; second, we utilize the global motion feature vector index histogram of a video shot as the global motion signature; third, video shot retrieval based on index histograms instead of original motion feature vectors guarantees the low computation complexity, and thus assures a real-time video shot retrieval. Experimental results show that the proposed scheme has high accuracy and low computation complexity.

In this Letter, we propose a blind and robust multiple watermarking scheme using Contourlet transform and singular value decomposition (SVD). The host image is first decomposed by Contourlet transform. Singular values of Contourlet coefficient blocks are adopted to embed watermark information, and a fast calculation method is proposed to avoid the heavy computation of SVD. The watermark is embedded in both low and high frequency Contourlet coefficients to increase the robustness against various attacks. Moreover, the proposed scheme intrinsically exploits the characteristics of human visual system and thus can ensure the invisibility of the watermark. Simulation results show that the proposed scheme outperforms other related methods in terms of both robustness and execution time.

This Letter presents a fast codeword search algorithm based on ordered Hadamard transform. Before encoding, the ordered Hadamard transform is performed offline on all codewords. During the encoding process, the ordered Hadamard transform is first performed on the input vector, and then a new inequality based on characteristic values of transformed vectors is used to reject the unlikely transformed codewords. Experimental results show that the algorithm outperforms many newly presented algorithms in the case of high dimensionality, especially for high-detail images.

The application of complex network theory to power grid analysis has been a hot topic in recent years, which mainly manifests itself in four aspects. The first aspect is to model power system networks. The second aspect is to reveal the topology of the grid itself. The third aspect is to reveal the inherent vulnerability and weakness of the power network itself and put forward the pertinent improvement measures to provide guidance for the construction of power grid. The last aspect is to analyze the mechanism of cascading failure and establish the cascading fault model of large power failure. In the past ten years, by using the complex network theory, many researchers have investigated the structural vulnerability of power grids from the point of view of topology. This letter studies the structural vulnerability of power grids according to the effect of selective node removal. We apply several kinds of node centralities including recently-presented second-order centrality (SOC) to guide the node removal attack. We test the effectiveness of all these centralities in guiding the node removal based on several IEEE power grids. Simulation results show that, compared with other node centralities, the SOC is relatively effective in guiding the node removal and can destroy the power grid with negative degree-degree correlation in less steps.

This letter proposes a mobile application framework named erasable photograph tagging (EPT) for photograph annotation and fast retrieval. The smartphone owner's voice is employed as tags and hidden in the host photograph without an extra feature database aided for retrieval. These digitized tags can be erased anytime with no distortion remaining in the recovered photograph.

This letter proposes a visible watermarking scheme for halftone images. It exploits HVS filtering to transform the image in binary domain into continuous-tone domain for watermark embedding. Then a codeword search operation converts the watermarked continuous-tone image into binary domain. The scheme is flexible for two weighting factors are involved to adjust the watermark embedding strength and the average intensity of the watermarked image. Moreover, it can be used in some applications where original continuous-tone images are not available and the halftoning method is unknown.

Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.

This letter proposes a fast k nearest neighbors search algorithm based on the wavelet transform. This technique exploits the important information of the approximation coefficients of the transform coefficient vector, from which we obtain two crucial inequalities that can be used to reject those vectors for which it is impossible to be k nearest neighbors. The computational complexity for searching for k nearest neighbors can be largely reduced. Experimental results on texture classification verify the effectiveness of our algorithm.